Pet owner evaluation system

ABSTRACT

Embodiments of a system and method for method for automatically determining pet owner interaction with a pet are generally described herein. A method may include obtaining sensor data including pet behavior metrics from a sensor, aggregating the sensor data, and interpreting the aggregated sensor data to determine an engagement factor of a pet owner to a pet. The method may include outputting a report based on the engagement factor or the aggregated sensor data.

BACKGROUND

It has been shown that high amounts of human social interaction protectselders against dementia and other chronic diseases. However, it isdifficult to objectively measure human social interactions. Some socialinteractions may occur online, such as over social media, or indifferent in-person locations. A person that has little face-to-facecontact may still have a rich social life online. Another person mayhave social interaction at a senior center or at a restaurant that isdifficult to measure. Even where possible, technical methods formeasuring face-to-face social interaction are often complex andcumbersome.

Elders in particular may be sensitive to the stigma of wearing healthrelated devices, or having such devices installed in their homes. Familyand staff may find it difficult to discuss detected changes in activitywith an elder who is fearful of losing independence.

According to some experts, pet ownership has been shown to provide anumber of health benefits to a pet owner. It is estimated by someexperts that over half of U.S. households have pets.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIG. 1 illustrates a display device for displaying pet and pet ownerinformation in accordance with some embodiments.

FIG. 2 illustrates a system for evaluating pet and pet owner interactionin accordance with some embodiments.

FIG. 3 illustrates a system for reporting pet and pet owner interactionin accordance with some embodiments.

FIG. 4 illustrates a flow chart showing a technique for outputting a petand pet owner interaction report in accordance with some embodiments.

FIG. 5 illustrates generally an example of a block diagram of a machineupon which any one or more of the techniques discussed herein mayperform in accordance with some embodiments.

DETAILED DESCRIPTION

Systems and methods for automatically tracking pet owner interactionwith one or more pets are described herein. The systems and methodsdescribed herein may include outputting a report indicating engagementbetween a pet owner and a pet. In the context of this disclosure, anowner may include any person or set of people who regularly interactwith an animal (e.g., the pet), such as members of a family (e.g.,parents, kids, other family members), caretakers of the animal (e.g., avet, a dog walker, etc.), pet play care people, animal shelter people,pet breeders, pet boarding employees, friends taking care of a pets,employees (such as police or guards) who interact with an employeeanimal in the course of their work (e.g., a police K9), trainers, ownerproxies such as show-dog handlers or horse show riders, or the like. Thepet may include any animal that may interact with a human.

Pet ownership has been shown to provide a number of health benefits tothe owner and pet ownership is on the rise. With the increase in petownership in the U.S., pet owners are looking to measure their pet'swellness. For example, there are a number of dog pedometers on themarket, designed to measure the amount of exercise the dog gets. Thesystems and methods disclosed herein aggregate data from devices, suchas dog pedometers, to measure the level of a pet's social interactionwith its owner(s). The systems and methods disclosed herein measure theamount of pet/owner social interaction, to gain insight into thewellness of the pet and/or the owner, and to enable caregiver-elderconversations to indirectly address the health of the elder bydiscussing the health of the pet.

The systems and methods disclosed herein include a new measure ofbehavior to be inferred and presented: a quantity (and optionally aquality) of social interaction between a pet and an owner. In anexample, sensors are used on the pet, the pet's environment, or objectsthat are used in pet/owner interaction, to calculate an aggregated valueof total pet/owner social interaction. That value may be used to providethe owner, the owner's family, or the owner's formal caregivers insightinto the social interactions of the owner and the pet. For example, adog collar may include a sensor that determines when the collar isattached to a leash. In combination with a dog pedometer, a system maymeasure the time the dog is walking while attached to the leash.

The time measured while the dog is walking and attached to the leash maybe used to determine a dog-owner social interaction time value, and isdistinguishable from just dog exercise time, such as when the dog isrunning around the house, possibly without owner interaction. The socialinteraction time value may be used to present values or trends in theinteraction time to the owner, the owner's family, or caregivers of theowner. The values or trends may be presented to the family or caregiverto converse with the owner about what caused the values or trends, orhow the owner might increase that dog interaction time. For example, theowner may be an elderly person. A large decrease in social time betweendog and owner may suggest an issue with the owner, such as depression,mobility issues, dementia or other chronic diseases.

Monitoring an elder using social interaction with a pet is lessstigmatizing than traditional methods. A system that monitors sensors onthe pet or the objects the pet interacts with rather than the elderdirectly is not overtly about the health or behavior of the elder. Thesystem enables the elder and their family/caregivers to frame the systemas “I'm fine, but I'm doing this to keep an eye on my dog's health.” Forexample, the elder may welcome discussions of how to get out of thehouse more with the elder's pet, while the elder may resist discussionsof how the elder might increase his or her own exercise.

Monitoring a pet owner using social interaction with a pet is lesscumbersome than traditional methods. In an example, the pet owner maynot wear any sensors. Further, because pet and pet owner socialinteraction may be mediated through specific objects (e.g., leash, tugtoy, etc.), sensors may be placed on those objects.

In another example, pet health may be a primary focus of monitoring petand pet owner interaction. For example, an owner, pet rescue group, or aprofessional pet breeder may be concerned with pet treatment by theowner or a pet sitter/walker. Measuring social interactions of the petmay provide an objective measure of the pet's quality of life. Forexample, a pet day-care space may evaluate the social wellness of a dogbefore accepting it into the day care. In another example, obediencetraining may be promoted as a way to develop a stronger social bondbetween a pet and pet owner. Pet training instructors may use measuresof changes in a pet's social interaction to illustrate the benefits ofthe training. For example, trends in social interaction may be monitoredto update a pet owner on the benefits of training when the owner spendsmore time with the pet after training.

The systems and methods described herein may monitor a pet that iscapable of interaction with humans. For example, people often own andinteract with dogs. A number of other types of pets may also bemonitored for interaction with humans, for example, cats, rabbits,birds, large “pasture pets” such as goats and horses, etc. Theinteraction of the pet with a human may include a specific person whointeracts with the pet or a set of people, for example a family orpet-play-care staff. To monitor the interactions between a pet and aperson or people, a sensor or set of sensors may be used. The sensor orsensors may be designed to detect significant behaviors, described inmore detail below.

The sensor or sensors may be used to send sensor data that may beaggregated by a processor to generate a measure of pet and human socialinteraction. For example, a quantity or a quality of human to petinteraction may be determined from the sensor data. In an example, theamount of time a sensor shows interaction between a pet and human may besummed. In another example, the sensor input may be weighted, forexample based on the type of sensor. In yet another example,interactions between a pet and human may be separated into low and highquality interactions. For example, filling a dog water or food bowl maybe considered a low quality interaction and playing with a dog may beconsidered a high quality interaction. Different types of interactionsmay be predetermined to have a specific weight or quality, which may bepredetermined by machine learning, user input, or designer input. In anexample, a processor may perform analysis on the sensor data and outputtrends or statistics. For example, the processor may calculate typicaltimes of interaction during the day or during the week. Changes to thetrends may be monitored and reported. In an example, a user interface(UI) may be used to show aspects of the social interaction determinedbetween a pet and an owner. For example, typical times of interactionduring the day or during the week may be displayed. In an example,aspects of the social interaction between a pet and an owner may be madeavailable to applications via an Application Programming Interface(API), for example a RESTful web interface. The API may enabling thoseapplications to use the data for a variety of purposes, such asqualifying potential pet owners or caretakers, sending messagestriggered by significant changes in social interaction, aggregation withother owner, or pet health data for use by a human or pet healthcareteam, etc. In an example, a user interface may query data from a web APIrather than directly querying a database.

The systems and methods described herein may detect when a pet isengaging with a human, not simply when the pet is in motion. Forexample, the amount and quality of a social interaction between the petand the human may be monitored and reported.

FIG. 1 illustrates a display device 100 for displaying pet and pet ownerinformation in accordance with some embodiments. The display device 100includes a display screen to display a user interface 101. The userinterface 101 may display a report, visualization, suggestion, or alert.For example, the user interface 101 includes a visual representation 102of an engagement factor of a pet owner to a pet. In an example, the userinterface 101 may include options to select one or more engagementfactors to display using the visual representation 102. For example,engagement factors may include social cues 104, daily ritual 106, play108, social interaction 110, overall 112, or other options not shown.The engagement factors may be determined using sensor data. In anexample, the overall 112 engagement factor may be a combination of oneor more of the other engagement factors. In another example, the overall112 engagement factor may include a weighted combination of one or moreof the other engagement factors.

The engagement factors 104-110 may represent interactions between a petowner and a pet. When a user selects one of the engagement factors104-110, the visual representation 102 may change according to theengagement factor that was selected. For example, if the social cues 104engagement factor is selected, the visual representation 102 may show asocial cue value. In an example, when the overall 112 engagement factoris selected, the visual representation 102 may show an aggregatedengagement factor, such as a combination (average or weighted) of theother engagement factors 104-110.

The social cues 104 engagement factor may include a score for a petowner based on the pet owner recognizing and interpreting social cuesfrom the pet. The social cues 104 engagement factor may focus onrecognizing events wherein the pet owner interpreted what the pet wantedor needed and the pet owner proceeded to act accordingly, or fail to actaccordingly. For example, a sensor may be used to determine that a dooropened, such as by a magnetic reed switch, motion switch, or other doormovement sensor in or by the door. Such a sensor may be part of a homesecurity system. A second sensor may be used to determine that the petwent through the door (e.g., was let out), such as by a radio frequencyidentification (RFID) sensor in a door frame and a pet collar containingan RFID tag to be sensed by the door, or by using a pet collar globalpositioning system (GPS) location sensor or other location sensor. Thecombination of the door-opening and pet-exit sensors may be correlatedand aggregated, such as by using timestamp data. This sensor combinationmay be more accurate than using only a pet-exit sensor, because in thelatter case the owner may leave the door ajar for the pet to come and gowithout any interaction between the pet and the owner. Similarly, anowner who is out running and has a pet that runs around the housewhenever the owner is out running may not include social interactionbetween the owner and the pet. By evaluating proximity between the ownerand the pet, interaction rather than simply the animal behavior may bemeasured. The social cues 104 engagement factor may reflect an inferencethat the owner observed cues from the pet, interpreted them to mean thatthe pet needed to go out, and then acted on those social cues by lettingthe pet outside. Additionally, an inference that the pet was let out,but was not promptly let back in, may suggest that the owner wasdistracted or impaired and that the pet may be put in danger from, forexample, prolonged exposure to hot or cold weather.

In an example, the social cues 104 engagement factor may includedetermining that a pet is inactive or has been inactive for a period oftime, such as by using an on-pet sensor (i.e., a sensor worn by the petor embedded in the pet) including, for example, an inertial sensor. Inanother example, determining that a pet is inactive may includedetermining a rapid transition of pet biosignals using a biosignalsensor (e.g., a heart rate sensor on a smart collar) to indicaterelaxation. The relaxation or inactivity of the pet may be used, inconjunction with sensing of the pet owner's motion and location, toinfer that a pet owner recognized that the pet wanted attention and paidattention to the pet, calming the pet.

In an example, interactions between a pet and an owner may becategorized. The categories may be used to weigh inputs, determinescores for different social interactions, or to classify sensor data.Sensor data may be used to quantify interactions that are categorizedaccording to qualitative measures. Some example categories appear below,including example scenarios.

A Daily Ritual Category—This category focuses on recognizing eventswhich are part of the patterns of daily living with the pet. In anexample, sensing inputs may include pet motion (a source may include apet worn inertial sensor), activity in food bowl (a source may includereadings from smart food bowl), weight of food bowl increases thendecreases (a source may include weight sensors under bowl). Thequantitative sensor data may be used to determine the quality of theinteraction between the owner and the pet, and in this case, forexample, to determine that the owner has fed the pet. In anotherexample, sensing inputs may include pet location or proximity (a sourcemay include a pressure sensor built into a smart pet bed), pet activity(a source may include a pet worn inertial sensor). The quality ofinteraction determined from these sensors may include an inference thatthe owner is awake and moving, as the pet is likely to wake up andfollow the owner when the owner leaves the bedroom, for example. Inanother example, the quality of interaction may show if the pet was leftoutside and not let back in right away, which may be a deviation fromthe daily pattern and also detrimental to the pet's health if the pet issensitive to weather or high or low temperatures.

A Play Category—This category focuses on inferring playful interactionsbetween a pet and an owner. In an example, sensing inputs may includepet activity (a source may include pet worn inertial sensors orbiosignals from internal pet sensors), pet toy activity (a source mayinclude a motion sensor embedded in a manual ball thrower, flying disk,or ball, or a tug toy with embedded stretch sensors). A determination inthis category may include that the pet and the owner were playing, orthat the pet initiated play with the owner or vice versa. In an example,motion classification techniques may be used to further refine thedetermination. For example, a motion classification technique may beused to determine whether a ball is thrown vertically or horizontally,or that the ball was dropped at an owner's feet. A motion classificationtechnique may determine whether a pet merely carried a ball (indicatinglow or potentially no interaction with the owner) or whether the ballwas thrown and then retrieved (indicating high interaction with theowner).

A Social Interaction Category—This category focuses on structuredinteraction with the pet over a span of continuous time. In an example,this category is different from recognizing and interpreting pet socialcues because it may be primarily evaluated against length of time spentin the interaction. In an example, sensing inputs may include pet bodyposition (a source may include a pet worn inertial sensor, a home camerastream, or a pet biosignal), such as when the pet body position shiftsrapidly. The social interaction category may be used in this example todetermine that an owner is going through commands with pet, and record aduration of interaction. In another example, sensing inputs may includepet location or proximity (a source may include a pet collar globalpositioning system (GPS) or Bluetooth low energy (BLE) beacon), ownerlocation (a source may include smartphone global navigation satellitesystem (GNSS) or assisted GPS (AGPS)), owner activity profile (a sourcemay include smartphone motion sensors), leash proximity (a source mayinclude RFID, capacitance, inductive sensors). From this sensor data, itmay be determined that an owner is walking the pet on a leash, andrecord a duration of interaction. In yet another example, sensing inputsmay include activity specific pet clothing, accessories, or equipmentusage (a source may include smart dog goggles, dog hiking pack,instrumented dog agility equipment), owner activity profile (a sourcemay include smartphone motion sensors). From this sensing data, it maybe determined that an owner is engaging in the activity specific to thepet equipment in use with the pet.

FIG. 2 illustrates a system 200 for evaluating pet and pet ownerinteraction in accordance with some embodiments. The system 200 includessource data component 202, an aggregation component 222, an engagementfactors component 230, and a reporting component 240.

The source data component 202 includes data from off-pet device sensors204, on-pet device sensors 214, and optionally, pet owner device(s) 221.The off-pet device sensors 204 may include a food or water bowl devicesensor 206, a pet accessory device sensor 208, a pet motion devicesensor 210, or a pet collaboration device sensor 212 (e.g., a pet toy,training equipment, such as buoys, bumpers, ropes, dummy birds (forretrieval), scent tins (for nose work), sledges, or other collaborationdevices for working dog breeds, or the like). The on-pet device sensors214 may include sensors on a pet wearable device. The on-pet devicesensors 214 may include a movement sensor, such as an accelerometer 216or a sensor for measurement of muscular activity directly (e.g., throughElectromyography), a pet biosignal sensor 218, or a GPS sensor 220. Datareceived from one or more of these sensors may be used to determine aninteraction score for a pet and an owner. The data may be sent to theaggregation component for aggregation.

The aggregation component 222 may include a subcomponent 224 toassociate the source data with a pet owner. The aggregation component222 may include a time, date, or location synchronization subcomponent226 to determine, correlate, or aggregate data according to time or datestamps, location, or proximity of a device or sensor outputting thedata. The aggregation component 222 may include a subcomponent 228 toaggregate owner device data, such as from the pet owner device(s) 221.

The engagement factors component 230 may include a plurality ofengagement factor evaluation categories, such as social cues activities232, daily ritual activities 234, play activities 236, or socialinteraction activities 238, as described above in relation to FIG. 1.These categories may use sensor data from the source data component 202that has been aggregated by the aggregation component 222. Weighting maybe added to the sensor data at the aggregation component 222 based onsensor source from the source data component 202 or at the engagementfactors component 230 using the categories. Information from theengagement factors component 230 may be sent to the reporting component240 for reporting to a user or may be sent to an interface (such as aRESTful web service or other API) using an API output subcomponent 252for providing data to external applications.

The reporting component 240 may include subcomponents to report trends242, visualizations 246, suggestions 248, or alerts 250. The reports maybe sent to the pet owner, a caretaker of the pet owner, an interestedthird party (e.g., a trainer of the pet, a family member of the owner,such as child away at college or a person concerned for an elderlyowner, etc.), or the like. The trends 242 may include daily, weekly, ormonthly schedules or typical activities, including information ondeviations from the schedules or typical activities. The trends 242 mayinclude a visual component, such as a trend line or graph. Thevisualizations 246 may include an overall social interaction score,depicted visually, a set of component scores, such as for one or more ofthe categories from the engagement factors component 230, or a graph orpicture depicting a score or set of scores (e.g., a smiley face whensocial interaction between the owner and the pet has been determined toexceed a threshold or a frowny face when the social interaction is belowthe threshold). The suggestions 248 may include recommendations forimproving social interaction between an owner and a pet. In an example,the suggestions 248 may include recommendations to a caretaker or familymember of the owner for checking up on the owner based on low socialinteraction scores with the pet. In another example, the suggestions 248may include recommended specific activities or general statements forimproving social interaction. In yet another example, the suggestions248 may include a grade for quality of care of the pet by the owner orquality of training of the pet by the owner, for example if the owner isa child trusted with the responsibilities of raising a pet or an ownertraining a pet through an obedience school, respectively. The alerts 250may include immediate or urgent alerts, such as to indicate a pet needsmedical attention or that an owner may need medical attention based on alack of care for the pet. In another example, the alerts 250 may be moreroutine, such as reminders to feed, give water to, exercise, or playwith the pet. The alerts 250 may be sent to the owner, family member,caretaker, interested third party, or the like. In yet another example,the alert 250 may indicate that a sensor has malfunctioned, is notoperating, that a battery is dead, or the like.

FIG. 3 illustrates a system 300 for reporting pet and pet ownerinteraction in accordance with some embodiments. The system 300 includesa receiver 302, an aggregator 304, an engagement factor component 306, atransmitter 308, and an optional comparator 310.

The receiver 302 may be used to obtain (e.g., retrieve or receive)sensor data including pet behavior metrics from a plurality of sensors,the plurality of sensors including at least one sensor of an on-petdevice and at least one sensor of an off-pet device. In an example, theon-pet device is a pet wearable device and the at least one sensor ofthe on-pet device includes at least one of an accelerometer, abiosensor, and a GPS sensor. In another example, the off-pet deviceincludes at least one of a pet toy device, a pet motion sensor device,and a pet bowl device. The aggregator 304 may be used to aggregate thesensor data over a predetermined period of time.

The engagement factor component 306 may be used to interpret theaggregated sensor data to automatically determine an aggregatedengagement factor of the pet owner to the pet, the aggregated engagementfactor corresponding to overall interaction between the pet owner andthe pet. The aggregated engagement factor may include a plurality ofengagement factors, and to automatically determine the aggregatedengagement factor may include automatically determining the aggregatedengagement factor from the plurality of engagement factors. Theengagement factor component 306 may further apply a weight to theplurality of engagement factors. In an example, the engagement factorcomponent 306 may interpret a second set of aggregated sensor data toautomatically determine a second aggregated engagement factor of the petowner to the pet. In another example, the engagement factor component306 may determine whether the second aggregated engagement factorexceeds the aggregated engagement factor.

The transmitter 308 drives an electrical signal on a physicalcommunications medium (e.g., a wire, a bus, a wired network such asEthernet, a network interface, a wireless connection, or the like). Thetransmitter 308 may be used to output a report indicating the aggregatedengagement factor. The transmitter 308 may output the report to a userwho is not the pet owner. The transmitter 308 may output data to anexternal interface using an API output such as a RESTful Web Service orother remote interface, in response to queries. The transmitter 308 may,in response to determining that the second aggregated engagement factorfalls below the aggregated engagement factor, alert the pet owner. Inanother example, the report may include a trend analysis based on theaggregated engagement factor and the second aggregated engagementfactor. The transmitter 308 may further provide a recommendation forimproving the aggregated engagement factor.

In an example, the pet behavior metrics may indicate a leash isconnected to a collar of the pet. In another example, a sensor of theplurality of sensors is a wearable device used by the pet owner, and thesensor data includes sensor data from the wearable device. To interpretthe aggregated sensor data to automatically determine the aggregatedengagement factor, the engagement factor component 306 may furtherdetermine that the sensor data from the wearable device includeslocation or proximity and timing data corresponding to the pet behaviormetrics, and the report may positively reflect that the location orproximity and timing data of sensor data from the wearable devicecorresponds to the pet behavior metrics.

The comparator 310 may be used to determine whether the aggregatedengagement factor falls below a baseline engagement factor. When theaggregated engagement factor falls below the baseline engagement factor,the transmitter 308 may output an alert indicating the aggregatedengagement factor fell below the baseline engagement factor. In anexample, the baseline engagement factor may be preset by the user who isnot the pet owner or by the pet owner. In another example, the baselineengagement factor may be determined using a machine learning technique.The machine learning technique may be used to determine a change inactivity of the pet owner over time. In another example, the transmitter308 may, in response to determining that the aggregated engagementfactor exceeds the baseline engagement factor, output an indication of ahealthy overall interaction.

The components illustrated and discussed in FIG. 2 may be implemented inone or more circuits or components of system 300, as described in FIG.3. The system 300 for reporting pet and pet owner interaction includes areceiver 302, an aggregator 304, an engagement factor component 306, atransmitter 308, and an optional comparator 310. The receiver 302, theaggregator 304, the engagement factor component 306, the transmitter308, or the optional comparator 310 are understood to encompass tangibleentities that are physically constructed, specifically configured (e.g.,hardwired), or temporarily (e.g., transitorily) configured (e.g.,programmed) to operate in a specified manner or to perform part or allof any operations described herein. Such tangible entitles may beconstructed using one or more circuits, such as with dedicated hardware(e.g., field programmable gate arrays (FPGAs), logic gates, graphicsprocessing unit (GPU), a digital signal processor (DSP), etc.). As such,the tangible entities described herein may be referred to as circuits,circuitry, processor units, subsystems, or the like.

FIG. 4 illustrates a flowchart showing a technique 400 for outputting apet and pet owner interaction report in accordance with someembodiments. The technique 400 includes an operation 402 to obtainsensor data from a plurality of sensors. The plurality of sensors mayinclude one or more sensors of an on-pet device (e.g., a wearabledevice, such as a collar, shirt, etc., including an accelerometer, abiosensor, a GPS sensor, or the like) and one or more sensors of anoff-pet device (e.g., a pet toy device, a pet motion sensor device, apet bowl device, or the like). The sensor data may include pet behaviormetrics, which may indicate a leash is connected to a collar of the pet.The technique 400 includes an operation 404 to aggregate the sensordata. The sensor data may be aggregated over a predetermined period oftime, such as a day.

The technique 400 includes an operation 406 to interpret the aggregatedsensor data to automatically determine an aggregated engagement factorof a pet owner to a pet. The aggregated engagement factor may correspondto overall interaction between the pet owner and the pet. For example,the aggregated engagement factor may be based on a plurality ofengagement factors, which may be weighted.

The technique 400 may include determining whether the aggregatedengagement factor falls below a baseline engagement factor, andoutputting, in response to determining that the aggregated engagementfactor falls below the baseline engagement factor, an alert indicatingthe aggregated engagement factor fell below the baseline engagementfactor. The baseline engagement factor may be preset by a user who isnot the pet owner or by the pet owner. In an example, the baselineengagement factor may be determined using a machine learning technique.The machine learning technique may be used to determine a change inactivity of the pet owner over time, such as a trend. In response todetermining that the aggregated engagement factor exceeds the baselineengagement factor, an indication of a healthy overall interactionbetween the pet owner and the pet may be generated.

In another example, the technique 400 may include operations tointerpret a second set of aggregated sensor data to automaticallydetermine a second aggregated engagement factor of the pet owner to thepet, and determine whether the second aggregated engagement factorexceeds the aggregated engagement factor. In response to determiningthat the second aggregated engagement factor falls below the aggregatedengagement factor, the technique 400 may include an operation to alertthe pet owner. The report may include a trend analysis based on theaggregated engagement factor and the second aggregated engagementfactor.

In an example, a sensor may be a wearable device used by the pet owner,and the sensor data may include sensor data from the wearable device.The wearable device may be coupled to a pet device (e.g., an on-petdevice, such as a smart collar). Operation 406 to interpret theaggregated sensor data may include determining that the sensor data fromthe wearable device includes a location and timing correspondence to thepet behavior metrics. The wearable device data and the pet behaviormetrics may be correlated. From the correlated data, the technique 400may include determining that the wearable data and the pet behaviormetrics indicate the pet owner and the pet were engaged in an activitytogether, and a positive report may be generated reflecting thecorresponding data.

The technique 400 includes an operation 408 to output a report. Thereport may include information sent to a user who is not the pet owner,such as a caretaker of the pet owner or other interested individual,such as a person indicated by the pet owner to receive the report. Thereport may include data sent to an external component in response to arequest from that external component, for example through a RESTful WebService or other remote interface (e.g., API). The report may includevisual metrics, an overall engagement factor value, a breakdown ofvalues for different engagement factors, etc. The report may include anotification on a mobile device, an email, or may be displayed on a userinterface of an app or application. The report may include an alert,user selectable options, trends, suggestions, or the like. The technique400 may include providing a recommendation for improving the aggregatedengagement factor.

FIG. 5 illustrates generally an example of a block diagram of a machine500 upon which any one or more of the techniques (e.g., methodologies)discussed herein may perform in accordance with some embodiments. Inalternative embodiments, the machine 500 may operate as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine 500 may operate in the capacity of aserver machine, a client machine, or both in server-client networkenvironments. The machine 500 may be a personal computer (PC), a tabletPC, a set-top box (STB), a personal digital assistant (PDA), a mobiletelephone, a web appliance, a network router, switch or bridge, or anymachine capable of executing instructions (sequential or otherwise) thatspecify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein, such as cloud computing, software asa service (SaaS), other computer cluster configurations.

Examples, as described herein, may include, or may operate on, logic ora number of components, modules, or mechanisms. Modules are tangibleentities (e.g., hardware) capable of performing specified operationswhen operating. A module includes hardware. In an example, the hardwaremay be specifically configured to carry out a specific operation (e.g.,hardwired). In an example, the hardware may include configurableexecution units (e.g., transistors, circuits, etc.) and a computerreadable medium containing instructions, where the instructionsconfigure the execution units to carry out a specific operation when inoperation. The configuring may occur under the direction of theexecutions units or a loading mechanism. Accordingly, the executionunits are communicatively coupled to the computer readable medium whenthe device is operating. In this example, the execution units may be amember of more than one module. For example, under operation, theexecution units may be configured by a first set of instructions toimplement a first module at one point in time and reconfigured by asecond set of instructions to implement a second module.

Machine (e.g., computer system) 500 may include a hardware processor 502(e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 504 and a static memory 506, some or all of which may communicatewith each other via an interlink (e.g., bus) 508. The machine 500 mayfurther include a display unit 510, an alphanumeric input device 512(e.g., a keyboard), and a user interface (UI) navigation device 514(e.g., a mouse). In an example, the display unit 510, alphanumeric inputdevice 512 and UI navigation device 514 may be a touch screen display.The machine 500 may additionally include a storage device (e.g., driveunit) 516, a signal generation device 518 (e.g., a speaker), a networkinterface device 520, and one or more sensors 521, such as a globalpositioning system (GPS) sensor, compass, accelerometer, or othersensor. The machine 500 may include an output controller 528, such as aserial (e.g., universal serial bus (USB), parallel, or other wired orwireless (e.g., infrared (IR), near field communication (NFC), etc.)connection to communicate or control one or more peripheral devices(e.g., a printer, card reader, etc.).

The storage device 516 may include a machine readable medium 522 that isnon-transitory on which is stored one or more sets of data structures orinstructions 524 (e.g., software) embodying or utilized by any one ormore of the techniques or functions described herein. The instructions524 may also reside, completely or at least partially, within the mainmemory 504, within static memory 506, or within the hardware processor502 during execution thereof by the machine 500. In an example, one orany combination of the hardware processor 502, the main memory 504, thestatic memory 506, or the storage device 516 may constitute machinereadable media.

While the machine readable medium 522 is illustrated as a single medium,the term “machine readable medium” may include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) configured to store the one or moreinstructions 524.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 500 and that cause the machine 500 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine readable medium examples mayinclude solid-state memories, and optical and magnetic media. Specificexamples of machine readable media may include: non-volatile memory,such as semiconductor memory devices (e.g., Electrically ProgrammableRead-Only Memory (EPROM), Electrically Erasable Programmable Read-OnlyMemory (EEPROM)) and flash memory devices; magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

The instructions 524 may further be transmitted or received over acommunications network 526 using a transmission medium via the networkinterface device 520 utilizing any one of a number of transfer protocols(e.g., frame relay, internet protocol (IP), transmission controlprotocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards, IEEE802.11.1 standards known as Bluetooth®, peer-to-peer (P2P) networks,among others. In an example, the network interface device 520 mayinclude one or more physical jacks (e.g., Ethernet, coaxial, or phonejacks) or one or more antennas to connect to the communications network526. In an example, the network interface device 520 may include aplurality of antennas to wirelessly communicate using at least one ofsingle-input multiple-output (SIMO), multiple-input multiple-output(MIMO), or multiple-input single-output (MISO) techniques. The term“transmission medium” shall be taken to include any intangible mediumthat is capable of storing, encoding or carrying instructions forexecution by the machine 500, and includes digital or analogcommunications signals or other intangible medium to facilitatecommunication of such software.

VARIOUS NOTES & EXAMPLES

Each of these non-limiting examples may stand on its own, or may becombined in various permutations or combinations with one or more of theother examples.

Example 1 is a system for automatically tracking pet owner interactionwith a pet, the system comprising: a receiver to obtain sensor dataincluding pet behavior metrics from a plurality of sensors, theplurality of sensors including at least one sensor of an on-pet deviceand at least one sensor of an off-pet device; an aggregator to aggregatethe sensor data over a predetermined period of time; an engagementfactor component to interpret the aggregated sensor data toautomatically determine an aggregated engagement factor of the pet ownerto the pet, the aggregated engagement factor corresponding to overallinteraction between the pet owner and the pet; and a transmitter tooutput a report indicating the aggregated engagement factor.

In Example 2, the subject matter of Example 1 optionally includeswherein to output the report, the transmitter is to output the report toa user who is not the pet owner.

In Example 3, the subject matter of Example 2 optionally includes acomparator to determine whether the aggregated engagement factor isbelow a baseline engagement factor; and wherein the transmitter isfurther to output, in response to determining that the aggregatedengagement factor is below the baseline engagement factor, an alertindicating the aggregated engagement factor is below the baselineengagement factor.

In Example 4, the subject matter of Example 3 optionally includeswherein the baseline engagement factor is preset by the user who is notthe pet owner.

In Example 5, the subject matter of any one or more of Examples 3-4optionally include wherein the baseline engagement factor is determinedusing a machine learning technique.

In Example 6, the subject matter of Example 5 optionally includeswherein the machine learning technique is used to determine a change inactivity of the pet owner over time.

In Example 7, the subject matter of any one or more of Examples 3-6optionally include wherein the transmitter is to, in response todetermining that the aggregated engagement factor exceeds the baselineengagement factor, output an indication of a healthy overallinteraction.

In Example 8, the subject matter of any one or more of Examples 1-7optionally include wherein the on-pet device is a pet wearable deviceand the at least one sensor of the on-pet device includes at least oneof a movement sensor, a biosensor, and a GPS sensor.

In Example 9, the subject matter of any one or more of Examples 1-8optionally include wherein the off-pet device includes at least one of apet collaboration device, a pet motion sensor device, and a pet bowldevice.

In Example 10, the subject matter of any one or more of Examples 1-9optionally include wherein the aggregated engagement factor includes aplurality of engagement factors, and wherein to automatically determinethe aggregated engagement factor includes to automatically determine theaggregated engagement factor from the plurality of engagement factors.

In Example 11, the subject matter of any one or more of Examples 1-10optionally include wherein the engagement factor component is further toapply a weight to the plurality of engagement factors.

In Example 12, the subject matter of any one or more of Examples 1-11optionally include wherein the engagement factor component is furtherto: interpret a second set of aggregated sensor data to automaticallydetermine a second aggregated engagement factor of the pet owner to thepet; and determine whether the second aggregated engagement factorexceeds the aggregated engagement factor.

In Example 13, the subject matter of Example 12 optionally includeswherein the transmitter is to, in response to determining that thesecond aggregated engagement factor is below the aggregated engagementfactor, alert the pet owner.

In Example 14, the subject matter of any one or more of Examples 12-13optionally include wherein the report includes a trend analysis based onthe aggregated engagement factor and the second aggregated engagementfactor.

In Example 15, the subject matter of any one or more of Examples 1-14optionally include wherein the pet behavior metrics indicate a leash isconnected to a collar of the pet.

In Example 16, the subject matter of any one or more of Examples 1-15optionally include wherein the transmitter is further to provide arecommendation for improving the aggregated engagement factor.

In Example 17, the subject matter of any one or more of Examples 1-16optionally include wherein a sensor of the plurality of sensors is awearable device used by the pet owner, and the sensor data includessensor data from the wearable device.

In Example 18, the subject matter of Example 17 optionally includeswherein to interpret the aggregated sensor data to automaticallydetermine the aggregated engagement factor, the engagement factorcomponent is further to determine that the sensor data from the wearabledevice includes location and timing data corresponding to the petbehavior metrics, and wherein the report positively reflects that thelocation and timing data of sensor data from the wearable devicecorresponds to the pet behavior metrics.

In Example 19, the subject matter of any one or more of Examples 1-18optionally include wherein to output the report, the transmitter is tooutput the report using an Application Programming Interface (API)protocol.

Example 20 is a method for automatically determining pet ownerinteraction with a pet, the method comprising: obtaining sensor dataincluding pet behavior metrics from a plurality of sensors, theplurality of sensors including at least one sensor of an on-pet deviceand at least one sensor of an off-pet device; aggregating the sensordata over a predetermined period of time; interpreting the aggregatedsensor data to automatically determine an aggregated engagement factorof the pet owner to the pet, the aggregated engagement factorcorresponding to overall interaction between the pet owner and the pet;and outputting a report indicating the aggregated engagement factor.

In Example 21, the subject matter of Example 20 optionally includeswherein outputting the report includes outputting the report to a userwho is not the pet owner.

In Example 22, the subject matter of Example 21 optionally includesdetermining whether the aggregated engagement factor is below a baselineengagement factor, and outputting, in response to determining that theaggregated engagement factor is below the baseline engagement factor, analert indicating the aggregated engagement factor is below the baselineengagement factor.

In Example 23, the subject matter of Example 22 optionally includeswherein the baseline engagement factor is preset by the user who is notthe pet owner.

In Example 24, the subject matter of any one or more of Examples 22-23optionally include wherein the baseline engagement factor is determinedusing a machine learning technique.

In Example 25, the subject matter of Example 24 optionally includeswherein the machine learning technique is used to determine a change inactivity of the pet owner over time.

In Example 26, the subject matter of any one or more of Examples 22-25optionally include in response to determining that the aggregatedengagement factor exceeds the baseline engagement factor, outputting anindication of a healthy overall interaction.

In Example 27, the subject matter of any one or more of Examples 20-26optionally include wherein the on-pet device is a pet wearable deviceand the at least one sensor of the on-pet device includes at least oneof a movement sensor, a biosensor, and a GPS sensor.

In Example 28, the subject matter of any one or more of Examples 20-27optionally include wherein the off-pet device includes at least one of apet collaboration device, a pet motion sensor device, and a pet bowldevice.

In Example 29, the subject matter of any one or more of Examples 20-28optionally include wherein the aggregated engagement factor includes aplurality of engagement factors, and wherein to automatically determinethe aggregated engagement factor includes to automatically determine theaggregated engagement factor from the plurality of engagement factors.

In Example 30, the subject matter of any one or more of Examples 20-29optionally include applying a weight to the plurality of engagementfactors.

In Example 31, the subject matter of any one or more of Examples 20-30optionally include interpreting a second set of aggregated sensor datato automatically determine a second aggregated engagement factor of thepet owner to the pet; and determining whether the second aggregatedengagement factor exceeds the aggregated engagement factor.

In Example 32, the subject matter of Example 31 optionally includes inresponse to determining that the second aggregated engagement factor isbelow the aggregated engagement factor, alerting the pet owner.

In Example 33, the subject matter of any one or more of Examples 31-32optionally include wherein the report includes a trend analysis based onthe aggregated engagement factor and the second aggregated engagementfactor.

In Example 34, the subject matter of any one or more of Examples 20-33optionally include wherein the pet behavior metrics indicate a leash isconnected to a collar of the pet.

In Example 35, the subject matter of any one or more of Examples 20-34optionally include providing a recommendation for improving theaggregated engagement factor.

In Example 36, the subject matter of any one or more of Examples 20-35optionally include wherein a sensor the plurality of sensors is awearable device used by the pet owner, and the sensor data includessensor data from the wearable device.

In Example 37, the subject matter of Example 36 optionally includeswherein interpreting the aggregated sensor data to automaticallydetermine the aggregated engagement factor includes determining that thesensor data from the wearable device includes a location and timingcorrespondence to the pet behavior metrics, and wherein the reportpositively reflects that the location and timing data of sensor datafrom the wearable device corresponds to the pet behavior metrics.

In Example 38, the subject matter of any one or more of Examples 20-37optionally include wherein outputting the report includes outputting thereport using an Application Programming Interface (API) protocol.

Example 39 is at least one machine-readable medium includinginstructions for operation of a computing system, which when executed bya machine, cause the machine to perform operations of any of the methodsof Examples 20-38.

Example 40 is an apparatus comprising means for performing any of themethods of Examples 20-38.

Example 41 is an apparatus for automatically determining pet ownerinteraction with a pet, the apparatus comprising: means for obtainingsensor data including pet behavior metrics from a plurality of sensors,the plurality of sensors including at least one sensor of an on-petdevice and at least one sensor of an off-pet device; means foraggregating the sensor data over a predetermined period of time; meansfor interpreting the aggregated sensor data to automatically determinean aggregated engagement factor of the pet owner to the pet, theaggregated engagement factor corresponding to overall interactionbetween the pet owner and the pet; and means for outputting a reportindicating the aggregated engagement factor.

In Example 42, the subject matter of Example 41 optionally includeswherein the means for outputting the report include means for outputtingthe report to a user who is not the pet owner.

In Example 43, the subject matter of Example 42 optionally includesmeans for determining whether the aggregated engagement factor is belowa baseline engagement factor, and means for outputting, in response todetermining that the aggregated engagement factor is below the baselineengagement factor, an alert indicating the aggregated engagement factoris below the baseline engagement factor.

In Example 44, the subject matter of Example 43 optionally includeswherein the baseline engagement factor is preset by the user who is notthe pet owner.

In Example 45, the subject matter of any one or more of Examples 43-44optionally include wherein the baseline engagement factor is determinedusing a machine learning technique.

In Example 46, the subject matter of Example 45 optionally includeswherein the machine learning technique is used to determine a change inactivity of the pet owner over time.

In Example 47, the subject matter of any one or more of Examples 43-46optionally include in response to determining that the aggregatedengagement factor exceeds the baseline engagement factor, means foroutputting an indication of a healthy overall interaction.

In Example 48, the subject matter of any one or more of Examples 41-47optionally include wherein the on-pet device is a pet wearable deviceand the at least one sensor of the on-pet device includes at least oneof a movement sensor, a biosensor, and a GPS sensor.

In Example 49, the subject matter of any one or more of Examples 41-48optionally include wherein the off-pet device includes at least one of apet collaboration device, a pet motion sensor device, and a pet bowldevice.

In Example 50, the subject matter of any one or more of Examples 41-49optionally include wherein the aggregated engagement factor includes aplurality of engagement factors, and wherein to automatically determinethe aggregated engagement factor includes to automatically determine theaggregated engagement factor from the plurality of engagement factors.

In Example 51, the subject matter of any one or more of Examples 41-50optionally include means for applying a weight to the plurality ofengagement factors.

In Example 52, the subject matter of any one or more of Examples 41-51optionally include means for interpreting a second set of aggregatedsensor data to automatically determine a second aggregated engagementfactor of the pet owner to the pet; and means for determining whetherthe second aggregated engagement factor exceeds the aggregatedengagement factor.

In Example 53, the subject matter of Example 52 optionally includes inresponse to determining that the second aggregated engagement factor isbelow the aggregated engagement factor, means for alerting the petowner.

In Example 54, the subject matter of any one or more of Examples 52-53optionally include wherein the report includes a trend analysis based onthe aggregated engagement factor and the second aggregated engagementfactor.

In Example 55, the subject matter of any one or more of Examples 41-54optionally include wherein the pet behavior metrics indicate a leash isconnected to a collar of the pet.

In Example 56, the subject matter of any one or more of Examples 41-55optionally include means for providing a recommendation for improvingthe aggregated engagement factor.

In Example 57, the subject matter of any one or more of Examples 41-56optionally include wherein a sensor the plurality of sensors is awearable device used by the pet owner, and the sensor data includessensor data from the wearable device.

In Example 58, the subject matter of any one or more of Examples 41-57optionally include wherein the means for interpreting the aggregatedsensor data to automatically determine the aggregated engagement factorinclude means for determining that the sensor data from the wearabledevice includes a location and timing correspondence to the pet behaviormetrics, and wherein the report positively reflects that the locationand timing data of sensor data from the wearable device corresponds tothe pet behavior metrics.

In Example 59, the subject matter of any one or more of Examples 41-58optionally include wherein the means for outputting the report includemeans for outputting the report using an Application ProgrammingInterface (API) protocol.

Method examples described herein may be machine or computer-implementedat least in part. Some examples may include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods may include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code may include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, in an example, the code may be tangiblystored on one or more volatile, non-transitory, or non-volatile tangiblecomputer-readable media, such as during execution or at other times.Examples of these tangible computer-readable media may include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

What is claimed is:
 1. A system for automatically tracking pet ownerinteraction with a pet, the system comprising: a receiver to obtainsensor data including pet behavior metrics from a plurality of sensors,the plurality of sensors including at least one sensor of an on-petdevice and at least one sensor of an off-pet device; an aggregator toaggregate the sensor data over a predetermined period of time; anengagement factor component, corresponding to a health status of the petowner, to interpret the aggregated sensor data to automaticallydetermine an aggregated engagement factor of the pet owner to the pet,the aggregated engagement factor corresponding to overall interactionbetween the pet owner and the pet, wherein the aggregated engagementfactor is based on a plurality of engagement factors; and a transmitterto output a report indicating the aggregated engagement factorcorresponding to the health status of the pet owner.
 2. The system ofclaim 1, wherein to output the report, the transmitter is to output thereport to a user who is not the pet owner.
 3. The system of claim 2,further comprising a comparator to determine whether the aggregatedengagement factor is below a baseline engagement factor; and wherein thetransmitter is further to output, in response to determining that theaggregated engagement factor is below the baseline engagement factor, analert indicating the aggregated engagement factor is below the baselineengagement factor.
 4. The system of claim 3, wherein the baselineengagement factor is preset by the user who is not the pet owner.
 5. Thesystem of claim 3, wherein the baseline engagement factor is determinedusing a machine learning technique.
 6. The system of claim 5, whereinthe machine learning technique is used to determine a change in activityof the pet owner over time.
 7. The system of claim 3, wherein thetransmitter is to, in response to determining that the aggregatedengagement factor exceeds the baseline engagement factor, output anindication of a healthy overall interaction.
 8. The system of claim 1,wherein the on-pet device is a pet wearable device and the at least onesensor of the on-pet device includes at least one of a movement sensor,a biosensor, and a GPS sensor.
 9. The system of claim 1, wherein theoff-pet device includes at least one of a pet collaboration device, apet motion sensor device, and a pet bowl device.
 10. The system of claim1, wherein to automatically determine the aggregated engagement factorincludes to automatically determine the aggregated engagement factorfrom the plurality of engagement factors.
 11. The system of claim 1,wherein the engagement factor component is further to apply a weight tothe plurality of engagement factors.
 12. The system of claim 1, whereinthe engagement factor component is further to: interpret a second set ofaggregated sensor data to automatically determine a second aggregatedengagement factor of the pet owner to the pet; and determine whether thesecond aggregated engagement factor exceeds the aggregated engagementfactor.
 13. The system of claim 12, wherein the transmitter is to, inresponse to determining that the second aggregated engagement factor isbelow the aggregated engagement factor, alert the pet owner.
 14. Thesystem of claim 12, wherein the report includes a trend analysis basedon the aggregated engagement factor and the second aggregated engagementfactor.
 15. The system of claim 1, wherein the pet behavior metricsindicate a leash is connected to a collar of the pet.
 16. The system ofclaim 1, wherein the transmitter is further to provide a recommendationfor improving the aggregated engagement factor.
 17. The system of claim1, wherein a sensor of the plurality of sensors is a wearable deviceused by the pet owner, and the sensor data includes sensor data from thewearable device.
 18. The system of claim 17, wherein to interpret theaggregated sensor data to automatically determine the aggregatedengagement factor, the engagement factor component is further todetermine that the sensor data from the wearable device includeslocation and timing data corresponding to the pet behavior metrics, andwherein the report positively reflects that the location and timing dataof sensor data from the wearable device corresponds to the pet behaviormetrics.
 19. The system of claim 1, wherein to output the report, thetransmitter is to output the report using an Application ProgrammingInterface (API) protocol.
 20. A method for automatically determining petowner interaction with a pet, the method comprising: obtaining sensordata including pet behavior metrics from a plurality of sensors, theplurality of sensors including at least one sensor of an on-pet deviceand at least one sensor of an off-pet device; aggregating the sensordata over a predetermined period of time; interpreting the aggregatedsensor data to automatically determine an aggregated engagement factorcorresponding to a health status of the pet owner, of the pet owner tothe pet, the aggregated engagement factor corresponding to overallinteraction between the pet owner and the pet, wherein the aggregatedengagement factor is based on a plurality of engagement factors; andoutputting a report indicating the aggregated engagement factor,corresponding to the health status of the pet owner.
 21. The method ofclaim 20, wherein outputting the report includes outputting the reportto a user who is not the pet owner.
 22. The method of claim 21, furthercomprising determining whether the aggregated engagement factor is belowa baseline engagement factor, and outputting, in response to determiningthat the aggregated engagement factor is below the baseline engagementfactor, an alert indicating the aggregated engagement factor is belowthe baseline engagement factor.
 23. At least one non-transitory machinereadable medium, including instructions, which when performed by amachine, cause the machine to: obtain sensor data including pet behaviormetrics from a plurality of sensors, the plurality of sensors includingat least one sensor of an on-pet device and at least one sensor of anoff-pet device; aggregate the sensor data over a predetermined period oftime; interpret the aggregated sensor data to automatically determine anaggregated engagement factor corresponding to a health status of the petowner, of the pet owner to the pet, the aggregated engagement factorcorresponding to overall interaction between the pet owner and the pet,wherein the aggregated engagement factor is based on a plurality ofengagement factors; and output a report indicating the aggregatedengagement factor corresponding to the health status of the pet owner.24. The at least one non-transitory machine readable medium of claim 23,further comprising instructions to apply a weight to the plurality ofengagement factors.
 25. The at least one non-transitory machine readablemedium of claim 23, further comprising instructions to provide arecommendation for improving the aggregated engagement factor.